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010   $a3-540-31351-6
024 7 $a10.1007/11615576
035   $a(CKB)1000000000232771
035   $a(SSID)ssj0000316987
035   $a(PQKBManifestationID)11258588
035   $a(PQKBTitleCode)TC0000316987
035   $a(PQKBWorkID)10287065
035   $a(PQKB)11372535
035   $a(DE-He213)978-3-540-31351-9
035   $a(MiAaPQ)EBC3068391
035   $a(PPN)123130662
035   $a(EXLCZ)991000000000232771
100   $a20100417d2006      u|             0
101 0 $aeng
135   $aurnn|008mamaa
181   $ctxt
182   $cc
183   $acr
200 10$aConstraint-Based Mining and Inductive Databases$b[electronic resource] $eEuropean Workshop on Inductive Databases and Constraint Based Mining, Hinterzarten, Germany, March 11-13, 2004, Revised Selected Papers /$fedited by Jean-Francois Boulicaut, Luc De Raedt, Heikki Mannila
205   $a1st ed. 2006.
210  1$aBerlin, Heidelberg :$cSpringer Berlin Heidelberg :$cImprint: Springer,$d2006.
215   $a1 online resource (X, 404 p.) 
225 1 $aLecture Notes in Artificial Intelligence ;$v3848
300   $aBibliographic Level Mode of Issuance: Monograph
311   $a3-540-31331-1 
320   $aIncludes bibliographical references and index.
327   $aThe Hows, Whys, and Whens of Constraints in Itemset and Rule Discovery -- A Relational Query Primitive for Constraint-Based Pattern Mining -- To See the Wood for the Trees: Mining Frequent Tree Patterns -- A Survey on Condensed Representations for Frequent Sets -- Adaptive Strategies for Mining the Positive Border of Interesting Patterns: Application to Inclusion Dependencies in Databases -- Computation of Mining Queries: An Algebraic Approach -- Inductive Queries on Polynomial Equations -- Mining Constrained Graphs: The Case of Workflow Systems -- CrossMine: Efficient Classification Across Multiple Database Relations -- Remarks on the Industrial Application of Inductive Database Technologies -- How to Quickly Find a Witness -- Relevancy in Constraint-Based Subgroup Discovery -- A Novel Incremental Approach to Association Rules Mining in Inductive Databases -- Employing Inductive Databases in Concrete Applications -- Contribution to Gene Expression Data Analysis by Means of Set Pattern Mining -- Boolean Formulas and Frequent Sets -- Generic Pattern Mining Via Data Mining Template Library -- Inductive Querying for Discovering Subgroups and Clusters.
410  0$aLecture Notes in Artificial Intelligence ;$v3848
606   $aArtificial intelligence
606   $aComputers
606   $aDatabase management
606   $aInformation storage and retrieval
606   $aPattern recognition
606   $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000
606   $aComputation by Abstract Devices$3https://scigraph.springernature.com/ontologies/product-market-codes/I16013
606   $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024
606   $aInformation Storage and Retrieval$3https://scigraph.springernature.com/ontologies/product-market-codes/I18032
606   $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X
615  0$aArtificial intelligence.
615  0$aComputers.
615  0$aDatabase management.
615  0$aInformation storage and retrieval.
615  0$aPattern recognition.
615 14$aArtificial Intelligence.
615 24$aComputation by Abstract Devices.
615 24$aDatabase Management.
615 24$aInformation Storage and Retrieval.
615 24$aPattern Recognition.
676   $a005.74
702   $aBoulicaut$b Jean-Francois$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aDe Raedt$b Luc$4edt$4http://id.loc.gov/vocabulary/relators/edt
702   $aMannila$b Heikki$4edt$4http://id.loc.gov/vocabulary/relators/edt
906   $aBOOK
912   $a996466097903316
996   $aConstraint-Based Mining and Inductive Databases$9772146
997   $aUNISA